Multi-objective combinatorial optimization problems in transportation and defense systems

Multi-objective Optimization problems arise in many applications; hence, solving them efficiently is important for decision makers. A common procedure to solve such problems is to generate the exact set of Pareto efficient solutions. However, if the problem is combinatorial, generating the exact set of Pareto efficient solutions can be challenging. This dissertation is dedicated to Multi-objective Combinatorial Optimization problems and their applications in system of systems architecting and railroad track inspection scheduling. In particular, multi-objective system of systems architecting problems with system flexibility and performance improvement funds have been investigated. Efficient solution methods are proposed and evaluated for not only the system of systems architecting problems, but also a generic multi-objective set covering problem. Additionally, a bi-objective track inspection scheduling problem is introduced for an automated ultrasonic inspection vehicle. Exact and approximation methods are discussed for this bi-objective track inspection scheduling problem --Abstract, page iii

Koopman representations for positive definite functions

We show that for any locally compact second countable group $G$ and any continuous positive definite function $\phi:G\rightarrow\mathbb{C}$, there exists an ergodic measure preserving system $(X,\mathscr{B},\mu,\{T_g\}_{g \in G})$ and a function $f \in L^2(X,\mu)$ for which $\phi(g) = \langle T_gf,f\rangle$. We also show that if $G$ is a countably infinite abelian group, then there exists a (not necessarily ergodic) measure preserving system $(X,\mathscr{B},\mu,\{T_g\}_{g \in G})$ and a function $f \in L^2(X,\mu)$ with $|f| = \phi(0)$ and $\phi(g) = \langle T_gf,f\rangle$.Comment: 13 page

Nano TiO2/Graphene Composites for Photovoltaic and Photocatalytic Materials

Graphene has been recognized as one of the most exciting carbon based materials of the present decade due to its unique electronic, mechanical and thermal properties. High surface area exfoliated graphene sheets with controllable surface functionality is an attractive two-dimensional surface for attaching different metals and semiconductors for improving the performance of catalysts, sensors, photoelectronic and energy conversion devices. Graphene is an ideal material which can be used for improving various metal oxide properties such as those of titania (TiO2). TiO2/graphene composites have shown excellent properties compared to bare TiO2 in various applications. In high quality graphene sheets, electrons can travel without scattering at room temperature making them potentially ideal electron transfer bridges, that can also act as an extended charge carrier network resulting in the potential for reduced electron-hole recombination rates when in direct contact with TiO2. The absorbance capability of TiO2 has also been improved when anchored onto highly porous graphene. By chemical modification of these networks, the surface properties can be adjusted for using the composites for tailored applications. This research has focused on the synthesis and modification of TiO2 nanomaterials on the surface of graphene sheets via an acid modified sol-gel process in supercritical carbon dioxide (scCO2). Acetic acid was used as the polycondensation agent while titanium isopropoxide was used as the Ti alkoxide. The resultant materials were characterized by electron microscopy (SEM and TEM), N2 physisorption, FTIR, XRD, XPS, thermal analysis, Raman spectroscopy, UV-Vis and PL analysis. The results showed that functionalized graphene sheets containing carboxylate groups acted as templates for anchoring modified TiO2 nanoparticles and nanowires on the surface. First, sonication following by scCO2 washing was used to anchor commercial anatase TiO2 on the graphene. TiO2 nanowires were then synthesized by using a sol-gel method in the green solvent scCO2 using titanium alkoxides. Uniform TiO2 nanowires with diameters less than 40 nm were decorated on the surface of the graphene sheets. One-dimensional, precisely oriented TiO2 nanostructures are more effective than TiO2 nanoparticles as their percolation pathways are excellent for charge transfer. Fe doped TiO2 morphologies on the graphene sheets were also prepared in both organic solvents and scCO2 to extend these devices band gap into the visible region. TiO2 nanoparticles less than 5 nm were uniformly formed on the graphene sheets when ethanol was used as the solvent. Doped TiO2 nanoparticles and nanowires showed smaller crystal size, higher visible absorption, higher surface area and higher thermal stability compared to similar materials without graphene. However, Fe doped nanowires prepared in scCO2 showed higher surface area and photocatalytic activity than those prepared in organic solvents. ZrO2-TiO2 bimetallic nanomaterials were also synthesized on the graphene sheets in scCO2 with different morphologies including nanofibers and nanotubes depending on the initial concentrations of precursors. The synthesized materials were examined for both photocatalytic and photovoltaic performance, with both materials providing higher activity than bare TiO2 and corresponding materials without graphene. Possible interactions between TiO2 and Fe doped TiO2 with graphene sheets and functionalized graphene sheets were studied by calculating adsorption energy values using the Vienna ab-initio Simulation Package (VASP) based on density functional theory (DFT). The results showed that both physical and chemical interactions are present and responsible for stable interaction between TiO2 and graphene sheets. The resulting modified TiO2 nanostructured materials on graphene sheets exhibited a higher visible adsorption, higher surface area, smaller crystallite size, and greater thermal stability, which are all desirable features for catalysts, support materials, semiconductors, and electrodes in dye-sensitized solar cells (DSSC). Nanomaterials prepared by these simple, scalable, environmentally friendly synthesis procedures are potentially attractive for commercial employment

DHLP 1&2: Giraph based distributed label propagation algorithms on heterogeneous drug-related networks

Background and Objective: Heterogeneous complex networks are large graphs consisting of different types of nodes and edges. The knowledge extraction from these networks is complicated. Moreover, the scale of these networks is steadily increasing. Thus, scalable methods are required. Methods: In this paper, two distributed label propagation algorithms for heterogeneous networks, namely DHLP-1 and DHLP-2 have been introduced. Biological networks are one type of the heterogeneous complex networks. As a case study, we have measured the efficiency of our proposed DHLP-1 and DHLP-2 algorithms on a biological network consisting of drugs, diseases, and targets. The subject we have studied in this network is drug repositioning but our algorithms can be used as general methods for heterogeneous networks other than the biological network. Results: We compared the proposed algorithms with similar non-distributed versions of them namely MINProp and Heter-LP. The experiments revealed the good performance of the algorithms in terms of running time and accuracy.Comment: Source code available for Apache Giraph on Hadoo

Some integrative management strategies to decrease doses of herbicides in agriculture

Over dose using of herbicides is one of the major problems in crops and horticulture productions. Human food safety and economical production of agricultural products are the main target of new agronomy and plant scientists. Integrative management is one of the new programs for reducing herbicide doses in agriculture. This program includes many physiological and physicochemical methods for controlling herbicide uses in farms and orchards. This article explain some of these methods such as using surfactants, water quality in spraying, using magnetic fields, controlling the nitrogen content of soil, using a suitable formulation and powerful cultivars and genotypes in agriculture and the effects of this reduction in herbicide doses on plants behavior and weeds controlling. According to this method integrative management can be beneficial in crop production and farmers must be using of this management method in their farms.Â

Operationally Efficient Propulsion System Study (OEPSS) data book. Volume 4: OEPSS design concepts

This study was initiated to identify operations problems and cost drivers for current propulsion systems and to identify technology and design approaches to increase the operational efficiency and reduce operations costs for future propulsion systems. To provide readily usable data for the Advanced Launch System (ALS) program, the results of the OEPSS study have been organized into a series of OEPSS Data Books. This volume describes three propulsion concepts that will simplify the propulsion system design and significantly reduce operational requirements. The concepts include: (1) a fully integrated, booster propulsion module concept for the ALS that avoids the complex system created by using autonomous engines with numerous artificial interfaces; (2) an LOX tank aft concept which avoids potentially dangerous geysering in long LOX propellant lines; and (3) an air augmented, rocket engine nozzle afterburning propulsion concept that will significantly reduce LOX propellant requirements, reduce vehicle size and simplify ground operations and ground support equipment and facilities

Segmentation and classification of lung nodules from Thoracic CT scans : methods based on dictionary learning and deep convolutional neural networks.

Lung cancer is a leading cause of cancer death in the world. Key to survival of patients is early diagnosis. Studies have demonstrated that screening high risk patients with Low-dose Computed Tomography (CT) is invaluable for reducing morbidity and mortality. Computer Aided Diagnosis (CADx) systems can assist radiologists and care providers in reading and analyzing lung CT images to segment, classify, and keep track of nodules for signs of cancer. In this thesis, we propose a CADx system for this purpose. To predict lung nodule malignancy, we propose a new deep learning framework that combines Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) to learn best in-plane and inter-slice visual features for diagnostic nodule classification. Since a nodule\u27s volumetric growth and shape variation over a period of time may reveal information regarding the malignancy of nodule, separately, a dictionary learning based approach is proposed to segment the nodule\u27s shape at two time points from two scans, one year apart. The output of a CNN classifier trained to learn visual appearance of malignant nodules is then combined with the derived measures of shape change and volumetric growth in assigning a probability of malignancy to the nodule. Due to the limited number of available CT scans of benign and malignant nodules in the image database from the National Lung Screening Trial (NLST), we chose to initially train a deep neural network on the larger LUNA16 Challenge database which was built for the purpose of eliminating false positives from detected nodules in thoracic CT scans. Discriminative features that were learned in this application were transferred to predict malignancy. The algorithm for segmenting nodule shapes in serial CT scans utilizes a sparse combination of training shapes (SCoTS). This algorithm captures a sparse representation of a shape in input data through a linear span of previously delineated shapes in a training repository. The model updates shape prior over level set iterations and captures variabilities in shapes by a sparse combination of the training data. The level set evolution is therefore driven by a data term as well as a term capturing valid prior shapes. During evolution, the shape prior influence is adjusted based on shape reconstruction, with the assigned weight determined from the degree of sparsity of the representation. The discriminative nature of sparse representation, affords us the opportunity to compare nodules\u27 variations in consecutive time points and to predict malignancy. Experimental validations of the proposed segmentation algorithm have been demonstrated on 542 3-D lung nodule data from the LIDC-IDRI database which includes radiologist delineated nodule boundaries. The effectiveness of the proposed deep learning and dictionary learning architectures for malignancy prediction have been demonstrated on CT data from 370 biopsied subjects collected from the NLST database. Each subject in this database had at least two serial CT scans at two separate time points one year apart. The proposed RNN CAD system achieved an ROC Area Under the Curve (AUC) of 0.87, when validated on CT data from nodules at second sequential time point and 0.83 based on dictionary learning method; however, when nodule shape change and appearance were combined, the classifier performance improved to AUC=0.89